agentleFS
baobachbaobach/llm-wiki-vault/skills/lint/SKILL.md

lint

Skill0 starsChanged 18 days agoMITView on GitHub

Health-check the vault for broken links, links into raw/, orphan notes, malformed frontmatter, contradictions, and missing concept notes. Use when the user asks to lint, audit, or clean up the vault.

80 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use query, ingest and 9 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • ArtemXTechSkill

    query

    Query data from this vault. USE WHEN user asks about projects, clients, tasks, daily notes. Use grep to extract frontmatter - do NOT read full files.

    Also in 17 projects that use this

  • JaimeGBASkill

    ingest

    Digest an external source (article, paper, transcript, URL, book chapter) and integrate its content into the knowledge base

    Also in 26 projects that use this

  • PydanticSkill

    coverage

    Fetch coverage diff from Codecov for the current branch or a specific PR. Shows uncovered lines, patch coverage, and overall coverage change.

    Also in 4 projects that use this

  • seongjaeryuSkill

    save

    Complete and save your current thinking to Bindly

    Also in 5 projects that use this

  • BugRogerSkill

    project

    Project lifecycle management. Dispatches to Ada.

    Also in 4 projects that use this

  • AWSSkill

    issue

    Implement a GitHub issue end-to-end — branch, code, test, docs, and commit.

    Also in 4 projects that use this

The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 2 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 67 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run python3 wiki_lint.py .

    line 11
  2. Very specific:

    If the script is missing, check the same things with grep.

    line 12
  3. Distinctive:

    Read pairs of notes on the same subject and look for contradicting claims.

    line 15
  4. Distinctive:

    Look for concepts mentioned in several notes that have no note of their own.

    line 16
  5. Very specific:

    Write outputs/lint-<date>.md: fixes applied first, then findings by category, then anything you skipped or sampled instead of checking …

    line 20

“Common” is measured: kept by at least 6 in 10 of the 67 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

/lint

Hermes Agent: do not run this skill. Tell the user it belongs to Claude Code or Codex (see the roles in AGENTS.md).

  1. Run python3 wiki_lint.py . in the vault root for the mechanical checks. If the script is missing, check the same things with grep. Then run python3 wiki_index.py . --check. It writes nothing. Exit 1 means Index.md is out of date: report it first and suggest /wiki.
  2. Read pairs of notes on the same subject and look for contradicting claims.
  3. Look for concepts mentioned in several notes that have no note of their own.
  4. Take the write lock before the first write.
  5. Auto-fix only mechanical problems with one obvious answer (a link that is a typo of an existing note, broken YAML syntax). Report everything else.
  6. Write outputs/lint-<date>.md: fixes applied first, then findings by category, then anything you skipped or sampled instead of checking fully.
  7. Log one line, then release the lock.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.